code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: Optional[int] , lowercase_: Optional[Any]=False ) -> Union[str, Any]:
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
A__ : Union[str, Any] = len(... | 64 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 64 | 1 |
from __future__ import annotations
class _a :
'''simple docstring'''
def __init__( self , A__ ):
A__ : List[Any] = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at """
"""least one and the same number o... | 64 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Any = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
warnings.wa... | 64 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 1 |
def UpperCamelCase (lowercase_: float , lowercase_: float ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(lowercase_ ) * abs(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose... | 64 |
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for... | 64 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_i... | 64 |
from functools import lru_cache
@lru_cache
def UpperCamelCase (lowercase_: int ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 1 |
def UpperCamelCase (lowercase_: int = 2000000 ) -> int:
A__ : Optional[int] = [0 for i in range(n + 1 )]
A__ : List[str] = 1
A__ : str = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i... | 64 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 1 |
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: List[str] , lowercase_: Dict , lowercase_: Union[str, Any] ) -> Tuple:
if height >= 1:
move_tower(height - 1 , lowercase_ , lowercase_ , lowercase_ )
move_disk(lowercase_ , lowerc... | 64 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 1 |
from __future__ import annotations
from collections import deque
class _a :
'''simple docstring'''
def __init__( self , A__ ):
A__ : list[dict] = []
self.adlist.append(
{"""value""": """""", """next_states""": [], """fail_state""": 0, """outp... | 64 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFormerOnnxConf... | 64 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 64 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 1 |
def UpperCamelCase (lowercase_: List[Any] , lowercase_: List[Any] , lowercase_: str , lowercase_: Union[str, Any] , lowercase_: List[Any] , lowercase_: Dict ) -> Any:
if index == r:
for j in range(lowercase_ ):
print(data[j] , end=""" """... | 64 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 64 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A_ : Tuple = datasets.utils.logging.get_logger(__nam... | 64 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.se... | 64 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file for file in filepaths if file != f... | 64 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
i... | 64 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCamelCase (*lowercase_: Optional[int] , lowercase_: Optional[Union[Dict, Any]] = None , lowercase_: Dict=True , lowercase_: Tuple=2 ) -> Dict:
from .. imp... | 64 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A_ : Union[str, Any] = logg... | 64 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_t... | 64 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase (lowercase_: Optional[int] ) -> List[str]:
for param in module.parameters():
A__ : Union[str, Any] = False
def UpperCamelCase () -> List[str]:
A__ : Tuple = ... | 64 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str ... | 64 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils impor... | 64 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b29... | 64 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _a (__magic_name__ , unittest.TestCase ):
'''simple docs... | 64 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike... | 64 |
def UpperCamelCase (lowercase_: int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError("""Input value must be an 'int' type""" )
A__ : int = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import... | 64 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 64 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 | 1 |
from __future__ import annotations
class _a :
'''simple docstring'''
def __init__( self , A__ , A__ ):
A__ , A__ : Dict = text, pattern
A__ , A__ : Optional[int] = len(A__ ), len(A__ )
def __A ... | 64 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 64 | 1 |
import string
from math import logaa
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> int:
A__ : Union[str, Any] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" )
A__ ... | 64 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 64 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase () -> Any:
A__ : Any = HfArgumentParser(lowercase_ )
A__ : List[Any] = parser.parse_args_into_dataclasses()[0]
A__ : str = TensorFlowBenchm... | 64 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from... | 64 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: str = (IPNDMScheduler,)
UpperCAmelCase__: Union[str, Any] = (... | 64 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 1 |
def UpperCamelCase (lowercase_: float , lowercase_: float ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main_... | 64 |
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for... | 64 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 64 |
from functools import lru_cache
@lru_cache
def UpperCamelCase (lowercase_: int ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
A_ : int = logging.get_logger(__name__)
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> st... | 64 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A_ : Tuple = datasets.utils.logging.get_logger(__nam... | 64 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDe... | 64 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 64 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Tuple = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class _a (__magic_name__ ):... | 64 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 1 |
from ..utils import DummyObject, requires_backends
class _a (metaclass=__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: Dict = ['''onnx''']
def __init__( self , *A__ , **A__ ):
requires_backends(self , ["""onnx"""] )
... | 64 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 | 1 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A_ : Tuple = datasets.utils.logging.get_logger(__nam... | 64 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSch... | 64 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file for file in filepaths if file != f... | 64 | 1 |
def UpperCamelCase (lowercase_: int | float | str ) -> tuple[int, int]:
try:
A__ : Any = float(lowercase_ )
except ValueError:
raise ValueError("""Please enter a valid number""" )
A__ : int = decimal - int(lowercase_ )
if fractional_part == 0:
... | 64 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCamelCase (*lowercase_: Optional[int] , lowercase_: Optional[Union[Dict, Any]] = None , lowercase_: Dict=True , lowercase_: Tuple=2 ) -> Dict:
from .. imp... | 64 | 1 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : str = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
Up... | 64 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_t... | 64 | 1 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str ... | 64 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b29... | 64 | 1 |
from __future__ import annotations
from typing import Any
class _a :
'''simple docstring'''
def __init__( self , A__ , A__ , A__ = 0 ):
A__ , A__ : Any = row, column
A__ : Tuple = [[default_value for c in range(A__ )]... | 64 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 64 |
def UpperCamelCase (lowercase_: int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError("""Input value must be an 'int' type""" )
A__ : int = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import... | 64 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 64 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 | 1 |
import argparse
import struct
import unittest
class _a :
'''simple docstring'''
def __init__( self , A__ ):
A__ : Optional[int] = data
# Initialize hash values
A__ : Any = [
0x6A_09E_667,
0xBB_67A_E85,
... | 64 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 64 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCamelCase (lowercase_: Dataset , lowercase_: Dict[str, str] ) -> List[str]:
A__ : int ... | 64 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 64 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
A_ : Dict = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],... | 64 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impor... | 64 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
A_ : List[Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
warnings.warn(
... | 64 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 1 |
from math import sqrt
def UpperCamelCase (lowercase_: int ) -> int:
A__ : List[str] = 0
for i in range(1 , int(sqrt(lowercase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase_ ):
total += i + n // i
elif i == sqrt(lowercase_ ):
total += i
... | 64 |
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for... | 64 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 |
from functools import lru_cache
@lru_cache
def UpperCamelCase (lowercase_: int ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
A_ : Dict = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, he... | 64 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 1 |
from __future__ import annotations
from math import pow, sqrt
def UpperCamelCase (lowercase_: float , lowercase_: float , lowercase_: float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0... | 64 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 64 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 64 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCamelCase (lowercase_: Union[str, Any] ) -> Tuple:
A__ : Dict = {}
A__ : Optional[int] = job["""started_at"""]
A__ : Tuple = job["""comple... | 64 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 1 |
def UpperCamelCase (lowercase_: int ) -> bool:
return str(lowercase_ ) == str(lowercase_ )[::-1]
def UpperCamelCase (lowercase_: int ) -> int:
return int(lowercase_ ) + int(str(lowercase_ )[::-1] )
def UpperCamelCase (lowercase_: int = 10000 ... | 64 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
A_ : List[str] = 3
def UpperCamelCase (lowercase_: int ) -> int:
print("""Generating primitive root of p""" )
while True:
A__ : Tuple = random.randrange... | 64 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A_ : Tuple = datasets.utils.logging.get_logger(__nam... | 64 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_v... | 64 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file for file in filepaths if file != f... | 64 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def UpperCamelCase (lowercase_: Dict , lowercase_: ... | 64 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCamelCase (*lowercase_: Optional[int] , lowercase_: Optional[Union[Dict, Any]] = None , lowercase_: Dict=True , lowercase_: Tuple=2 ) -> Dict:
from .. imp... | 64 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 64 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_t... | 64 | 1 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str ... | 64 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _a (unittest.TestCase ):
'''simple docstring'''
def __A ( self ):
A__ : List[Any] = get_activation("""swish""" )
self.assert... | 64 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b29... | 64 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ... | 64 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: str = '''MCTCTFeatureExtractor'''
UpperCAmelCase__: Any = '''AutoTokenizer''... | 64 |
def UpperCamelCase (lowercase_: int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError("""Input value must be an 'int' type""" )
A__ : int = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[int] = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'Debert... | 64 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b29... | 64 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 64 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_... | 64 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 64 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 64 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 1 |
from __future__ import annotations
def UpperCamelCase (lowercase_: str , lowercase_: list[str] | None = None ) -> list[list[str]]:
A__ : Dict = word_bank or []
# create a table
A__ : int = len(lowercase_ ) + 1
A__ : list[list[list[str]]] ... | 64 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Optional[Any] = {
'xlm-roberta-base': 'https://hugging... | 64 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 64 |
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for... | 64 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...u... | 64 |
from functools import lru_cache
@lru_cache
def UpperCamelCase (lowercase_: int ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : int = logging.get_logger(__name__)
def UpperCamelCase (lowercase_: Tuple ) -> str:
A__ : Dict ... | 64 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 1 |
def UpperCamelCase (lowercase_: list , lowercase_: int = 0 ) -> list:
A__ : Any = length or len(lowercase_ )
A__ : List[str] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
A__ , A__ : Optional[Any]... | 64 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def UpperCamelCase () -> Union[str, Any]:
A__ ... | 64 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 64 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRET... | 64 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 1 |
from math import pi, sqrt, tan
def UpperCamelCase (lowercase_: float ) -> float:
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def UpperCamelCase (lowercase_: float , lowercase_: float , ... | 64 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 | 1 |
def UpperCamelCase (lowercase_: list ) -> float:
A__ : List[Any] = 0
while len(lowercase_ ) > 1:
A__ : Dict = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
A__ : List[str] = files.index(min(lowercase_ ... | 64 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A_ : Tuple = datasets.utils.logging.get_logger(__nam... | 64 | 1 |
from __future__ import annotations
def UpperCamelCase (lowercase_: Optional[Any] , lowercase_: List[Any] , lowercase_: int , lowercase_: Optional[int] , lowercase_: str , ) -> None:
A__ : Dict = len(SCREAMING_SNAKE_CASE_ )
# If row... | 700 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional[Any] = [file for file in filepaths if file != f... | 64 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Tuple = {
'facebook/data2vec-text-base': 'https://hugg... | 701 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCamelCase (*lowercase_: Optional[int] , lowercase_: Optional[Union[Dict, Any]] = None , lowercase_: Dict=True , lowercase_: Tuple=2 ) -> Dict:
from .. imp... | 64 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCamelCase (lowercase_: List[Any] = 8 ) -> str:
A__ : Optional[int] = ascii_letters + digits + punctuation
return "".join(secrets.choice(_SCREA... | 702 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_t... | 64 | 0 |
def UpperCamelCase (lowercase_: Dict ) -> str:
return "".join([hex(lowercase_ )[2:].zfill(2 ).upper() for byte in list(lowercase_ )] )
def UpperCamelCase (lowercase_: str ) -> bytes:
if (len(lowercase_ ) % 2) != 0:
raise ValueError(
"""Base16 en... | 703 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str ... | 64 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testin... | 704 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b29... | 64 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_availab... | 705 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImage... | 706 |
def UpperCamelCase (lowercase_: int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError("""Input value must be an 'int' type""" )
A__ : int = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import... | 64 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : List[str] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNot... | 707 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 | 0 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 708 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 64 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hu... | 709 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 64 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
A_ : Tuple = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1000,
"block_out... | 710 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 64 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, ran... | 711 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 0 |
import numpy as np
class _a :
'''simple docstring'''
def __init__( self ):
A__ : List[Any] = (0, 0)
A__ : Dict = None
A__ : List[str] = 0
A__ : Optional[int] = 0
A__ : int ... | 712 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_snake_case ):
'''simple docstring'''
UpperCAmelCase__: Tuple = ['image_processor', 'tokenizer']
UpperCAmelCase__: Tuple ... | 713 |
from __future__ import annotations
from collections.abc import Callable
A_ : List[Any] = list[list[float | int]]
def UpperCamelCase (lowercase_: Matrix , lowercase_: Matrix ) -> Matrix:
A__ : int = len(lowercase_ )
A__ : Matrix = [[0 for... | 64 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
A_ : Any = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
'funnel-transformer/small-base': 'https://hu... | 714 |
from functools import lru_cache
@lru_cache
def UpperCamelCase (lowercase_: int ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 0 |
from scipy.stats import pearsonr
import datasets
A_ : Any = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset ... | 715 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
A_ : str = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# S... | 716 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 0 |
import datasets
A_ : Any = '\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n an... | 717 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 64 | 0 |
import argparse
import os
import re
A_ : List[str] = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A_ : str = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
#... | 718 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ ... | 64 | 0 |
A_ : int = tuple[float, float, float]
A_ : Optional[int] = tuple[float, float, float]
def UpperCamelCase (lowercase_: Dict , lowercase_: Optional[int] ) -> Vectorad:
A__ : List[Any] = end_pointa[0] - end_pointa[0]
A__ : Optional[int] ... | 719 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 0 |
from collections.abc import Iterable
from typing import Any
class _a :
'''simple docstring'''
def __init__( self , A__ = None ):
A__ : Tuple = value
A__ : str = None # Added in order to delete a node easier
A__ : Optiona... | 720 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magi... | 64 | 0 |
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